README.md

IBM: Machine Learning with Python

INSTRUCTORS

Instructors: Saeed Aghabozorgi

Course Description

You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed!

Topics include

Module 1 - Introduction to Machine Learning

• Applications of Machine Learning
• Supervised vs Unsupervised Learning
• Python libraries suitable for Machine Learning

Module 2 - Regression

• Linear Regression
• Non-linear Regression
• Model evaluation methods

Module 3 - Classification

• K-Nearest Neighbour
• Decision Trees
• Logistic Regression
• Support Vector Machines
• Model Evaluation

Module 4 - Unsupervised Learning

• K-Means Clustering
• Hierarchical Clustering Density-Based Clustering

Module 5 - Recommender Systems

• Content-based recommender systems
• Collaborative Filtering

This course is part of the 'IBM Data Science Professional Certificate'